code: def super_lm_tpu():
      """Hyperparameters for data-parallel training on TPU.
  
      This is not the intended usage - we would really like to use model-parallelism
      with the model shards mapping to cores and cross_replica_sum used for
      communication.  Currently, we replicate the entire model on each core.
  
      Returns:
        An hparams object.
      """
      hparams = super_lm_base()
      hparams.batch_size = 4096
      return hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import diet
  from tensor2tensor.utils import expert_utils
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 11 for calling a global function with example usage: super_lm_tpu() and returns: hparams